چکیده (انگلیسی):

One of the challenges in natural language processing is to
determine which pronouns to be referred to their intended referents in the
discourse. Performing anaphora resolution is considered as an important task
for a number of natural language processing applications such as information
extraction, question answering and text summarization. Most of the earlier
works of anaphora resolution have been applied to English and other
languages. However, the work done in Arabic is not sufficiently studied. In
this study, a hybrid approach that combines different architectures for
resolving pronominal anaphora in Arabic language is presented. The hybrid
model adopted the strategy based on the combination of a rule-based and
machine learning approach. The collection of anaphora and respective
possible antecedents was identified in a rule-based manner with
morphological information taken into account. In addition, the selection of
the most probable candidate as the antecedent of the anaphor was done by
machine learning based on a k-Nearest Neighbor (k-NN) approach. In this
study, the appropriate features to be used in this task were determined and
their effect on the performance of anaphora resolution was investigated.
Experiments of the proposed method were performed using the corpus of the
Quran annotated with pronominal anaphora. The experimental results
indicate that the proposed hybrid approach is completely reasonable and
feasible for Arabic pronominal anaphora resolution.